Intra Seasonal Prediction and Predictability for Boreal Winter

The predictability of intraseasonal variation in the tropics is assessed in the present study by using various statistical and dynamical models with rigorous and fair measurements. For a fair comparison, the real-time multivariate Madden-Julian Oscillation (RMM) index, proposed by Wheeler and Hendon (2004), is used as a predictand for all models. The statistical models include the models based on a multi linear regression, a wavelet analysis, and a singular spectrum analysis (SSA). The prediction limits (correlation skill of 0.5) of statistical models for RMM 1 (RMM2) index are at day 16-17 (14-15) for the multi regression model, whereas, they are at day 8-10 (9-12) for the wavelet and SSA based models. The poor predictability of the wavelet and SSA models is related to the tapering problems for a half length of the time window before the initial condition. To assess the dynamical predictability, long-term serial prediction experiments with a prediction interval of every 5 days are carried out with both SNU AGCM and CGCM for the 26 (1980-2005) boreal winters. The prediction limits of RMMI and RMM2 occur at day around 20 days for both AGCM and CGCM. These results demonstrate that the skills of dynamical models used in this study are better than those of the three statistical predictions. The dynamical and statistical predictions are combined using a multi-model ensemble method. The combination provides a superior skill to any of the statistical and dynamical predictions with a prediction limit of22-24 days.